HAL-Université de Bretagne Occidentale
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Visualisation des ondes sonores dans l'eau par cymatique.: Cymaglyphes intégrés dans un film documentaire
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Adaptive gradient domain normalization for one-sided unsupervised medical image synthesis
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New directions for interconnector research: drawing from social sciences and humanities perspectives to explore the Celtic Interconnector
No full textInternational audienceThe current discourse on interconnectors primarily centers on the technical and economic aspects necessary for delivering a stable grid infrastructure powered by renewable sources and for integrating energy markets. This article, therefore, explores opportunities to broaden definitions of energy grid interconnectivity beyond the techno-economic sphere. It considers multidisciplinary perspectives and presents novel exploratory viewpoints from the social sciences and humanities. It examines ideas of interconnection by drawing on the Celtic Interconnector, an Irish-French initiative, to explore the cultural, historical, political, and geographical dimensions of interconnectivity. Insights are derived from two workshops with academics in Ireland and France, encouraging a more contextual understanding of energy interconnections beyond their physical and economic dimensions. The article builds on these insights to set out an agenda for future research and reflect on frames of reference for describing, analysing, and engaging with emerging interconnector processes and the multiple stakeholders involved
Intérêts didactiques d’un modèle précurseur dans l’enseignement et l’apprentissage des sciences à l’école primaire
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Comment caractériser l'autonomie, la coopération, la créativité et l'équité sociale et de genre dans des séances d’enseignement orientées investigation en sciences ? Retour sur l’élaboration d’une grille pluridimensionnelle
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Grotesque Hordes That Matter: Biopower, Contamination and the Masses in Contemporary Zombie Narratives
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Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts
No full textInternational audienceSemantic segmentation, the process of assigning a semantic label to each pixel in an image, is a critical computer vision task for extracting detailed information from remote sensing data. However, its application to complex geographical contexts, such as coastal wetlands, is often constrained by the need for highly specialized implementations, class imbalance, and limited accessibility for non-specialists. This paper introduces a novel, modular, and adaptive open-source framework for semantic segmentation tailored to satellite imagery. Designed for maximum flexibility, the framework supports both binary and multi-class segmentation tasks and incorporates specific training strategies to handle severe class imbalances inherent in ecological detection, such as salt marsh mapping. The implementation provides a fully configurable pipeline that bridges the gap between Geographic Information Systems (GIS) and Deep Learning (DL). It integrates QGIS for intuitive spatial preprocessing and grid generation with a Python-based training and prediction workflow, thereby democratizing access to advanced segmentation techniques. The framework is architecture-agnostic, allowing the seamless deployment and benchmarking of various state-of-the-art encoder-decoder models, which are effective at combining multi-scale contextual information with high spatial resolution. A key contribution is the integration of a multifaceted training methodology that includes hybrid loss functions with dynamic class weighting and spectral-consistent data augmentation to ensure robust model generalization from limited and imbalanced datasets. We demonstrate the framework's efficacy and scalability through two distinct case studies: a multi-class land cover classification on the Crozon Peninsula using Pléiades and a binary salt marsh detection in the Mont-Saint-Michel Bay Sentinel-2 imagery. The results show that accurate segmentation can be achieved with modest computational resources, promoting more sustainable and ethical AI applications in environmental monitoring. This work provides a critical tool for researchers and practitioners aiming to apply advanced DL segmentation to domain specific remote sensing challenges beyond conventional benchmarks
Influence of precursor molecular weight on swelling and elastic modulus of cross-linked PDMS films
No full textInternational audienceThe influence of precursor molecular weight on cross-linked poly(dimethylsiloxane) (PDMS) thin films was studied at constant cross-linker ratio. Increasing molecular weight lowers cross-link density, enhances swelling, and reduces elastic modulus
Effect of a home-based calf neuromuscular electrical stimulation program on walking capacity in adults with lower-extremity PAD: the ELECTRO-PAD RCT
No full textInternational audienceAim: To evaluate whether a 3-month home-based neuromuscular electrical stimulation (NMES) program improved maximal walking distance (MWD) in patients with lower-extremity peripheral artery disease (PAD) compared with usual care.Methods: This multicenter randomized clinical trial included 73 adults with PAD and walking impairment from five French university hospitals between September 2019 and July 2022, with final follow-up in November 2022. Participants were randomized to a NMES group (n = 34) or a usual care group (n = 39) for 3 months. Both groups received standard medical management and lifestyle advice; the NMES group performed calf NMES sessions at home for 2-3 hours/day, 5 days/week. The primary outcome was the 3-month change in treadmill MWD. Secondary outcomes included change in 6-minute total walking distance.Results: Of the 73 randomized participants (mean [SD] age, 62.8 [9.2] years; 14 [19.2%] women), 64 (88%) completed the 3-month follow-up. Mean treadmill MWD increased from 148.5 m at baseline to 254.1 m at 3 months in the NMES group and from 142.5 m to 175.8 m in the usual care group (between-group difference, 69.9 m [95% CI, 10.8-129.0 m]; P = .02). The 6-minute total walking distance improved from 371.0 m to 405.4 m with NMES and from 342.5 m to 348.8 m with usual care (between-group difference, 29.6 m [95% CI, 5.1-54.1 m]; P = .02).Conclusions: In patients with PAD and walking impairment, a 3-month home-based calf NMES program significantly improved walking capacity. Further studies are needed to assess long-term effects.Trial registration: ClinicalTrials.gov identifier: NCT03795103